Year: 2016
Type: article
Abstract: Many cells can sense and respond to time-varying stimuli, selectively triggering changes in cell fate only in response to inputs of a particular duration or frequency. A common motif in dynamically controlled cells is a dual-timescale regulatory network: although long-term fate decisions are ultimately controlled by a slow-timescale switch (e.g., gene expression), input signals are first processed by a fast-timescale signaling layer, which is hypothesized to filter what dynamic information is efficiently relayed downstream. Directly testing the design principles of how dual-timescale circuits control dynamic sensing, however, has been challenging, because most synthetic biology methods have focused solely on rewiring transcriptional circuits, which operate at a single slow timescale. Here, we report the development of a modular approach for flexibly engineering phosphorylation circuits using designed phospho-regulon motifs. By then linking rapid phospho-feedback with slower downstream transcription-based bistable switches, we can construct synthetic dual-timescale circuits in yeast in which the triggering dynamics and the end-state properties of the ON state can be selectively tuned. These phospho-regulon tools thus open up the possibility to engineer cells with customized dynamical control.
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: bronze
Funders: National Science Foundation, Howard Hughes Medical Institute, Cancer Research Institute, Jane Coffin Childs Memorial Fund for Medical Research, National Institutes of Health, Genentech, University of California, San Francisco, National Eye Institute, National Institute of General Medical Sciences
Awards: Exploring Design Principles of Cellular Control Circuits, G2562566396, G3219492996, G3660308695, G4985329915, Protein Recognition in Signal Transduction, Combinatorial Exploration of Cellular Regulatory Network Design Principles, G6995010428, G9000087760, G9000218453, G9000240123, G9000256350
OpenAlex ID: W2555032360